System and method for defining boundaries of a simulation of an electric aircraft

ABSTRACT

The system and method for defining boundaries of a simulation of an electric aircraft is illustrated. The system comprises a sensor, a computing device, and a remote device. The sensor is configured to detect an aircraft location datum, detect a boundary datum associated with a three-dimensional flying space, and transmit the aircraft location datum and boundary datum to a computing device. The computing device is configured to receive the aircraft location datum and boundary datum from the sensor, determine a distance datum between the aircraft location and boundary as a function of the aircraft location datum and boundary datum generate a recommended aircraft adjustment as a function of the distance datum, and transmit the distance datum and recommended aircraft adjustment to a remote device. The remote device is configured to receive the distance datum and recommended aircraft adjustment and display them to a user.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircrafts. In particular, the present invention is directed to systemand method for defining boundaries of a simulation of an electricaircraft.

BACKGROUND

It can be challenging for pilots to know how close they are to aboundary during flight time or during a flight simulation. Knowing theirown distance from the boundary helps the pilot have a safer, smoother,and more informed flight experience without reaching the limits of thearea they are flying in.

SUMMARY OF THE DISCLOSURE

In an aspect, the system for defining boundaries of a simulation of anelectric aircraft is illustrated. The system comprises a sensor, acomputing device, and a remote device. The sensor is configured todetect an aircraft location datum, detect a boundary datum associatedwith a three-dimensional flying space, and transmit the aircraftlocation datum and boundary datum to a computing device. The computingdevice is configured to receive the aircraft location datum and boundarydatum from the sensor, determine a distance datum between the aircraftlocation and boundary as a function of the aircraft location datum andboundary datum generate a recommended aircraft adjustment as a functionof the distance datum, and transmit the distance datum and recommendedaircraft adjustment to a remote device. The remote device is configuredto receive the distance datum and recommended aircraft adjustment anddisplay them to a user.

In another aspect, a method for defining boundaries of a simulation ofan electric aircraft is illustrated. The method comprises detecting, ata sensor, an aircraft location datum, detecting, at a sensor, a boundarydatum associated with a three-dimensional flying space, transmitting, ata sensor, the aircraft location datum and boundary datum to a computingdevice, receiving, at a computing device, aircraft location datum andboundary datum from the sensor, determining, at a computing device, adistance datum between the aircraft location and boundary as a functionof the aircraft location datum and boundary datum, generating, at acomputing device, a recommended aircraft adjustment as a function of thedistance datum, transmitting, at a computing device, the distance datumand recommended aircraft adjustment to a remote device, receiving, at aremote device, the distance datum and recommended aircraft adjustmentfrom the computing device, and displaying, at a remote device, thedistance datum and recommended aircraft adjustment to a user.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a diagrammatic representation of an exemplary embodiment of anelectric aircraft;

FIG. 2A is a diagrammatic representation of an exemplary embodiment of aside view of the cross-sectional area associated with the boundary ofthe flying space;

FIG. 2B is a diagrammatic representation of an exemplary embodiment of atop view of the cross-sectional area associated with the boundary of theflying space;

FIG. 3 is a block diagram of an exemplary embodiment of a system fordetermining boundaries in a simulation of an electric aircraft;

FIG. 4 is a block diagram of an exemplary embodiment of a flightcontroller;

FIG. 5 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 6 is a flow diagram of an exemplary embodiment of a method fordetermining boundaries in a simulation of an electric aircraft; and

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. For purposes ofdescription herein, the terms “upper”, “lower”, “left”, “rear”, “right”,“front”, “vertical”, “horizontal”, and derivatives thereof shall relateto the invention as oriented in FIG. 1 . Furthermore, there is nointention to be bound by any expressed or implied theory presented inthe preceding technical field, background, brief summary or thefollowing detailed description. It is also to be understood that thespecific devices and processes illustrated in the attached drawings, anddescribed in the following specification, are simply exemplaryembodiments of the inventive concepts defined in the appended claims.Hence, specific dimensions and other physical characteristics relatingto the embodiments disclosed herein are not to be considered aslimiting, unless the claims expressly state otherwise.

At a high level, aspects of the present disclosure are directed to anaircraft with a visual system. In an embodiment, this disclosureincludes an aircraft configured to include a fuselage and a plurality offlight components attached to the fuselage. Aspects of the presentdisclosure include at least an exterior visual device to provide viewsof the exterior environment of the aircraft. Aspects of the presentdisclosure include at least a flight controller configured to receive aninput from the visual device and display it for the pilot. Exemplaryembodiments illustrating aspects of the present disclosure are describedbelow in the context of several specific examples.

Referring now to FIG. 1 , an exemplary embodiment of an aircraft 100 isillustrated. In an embodiment, aircraft 100 is an electric aircraft. Asused in this disclosure an “aircraft” is any vehicle that may fly bygaining support from the air. As a non-limiting example, aircraft mayinclude airplanes, helicopters, commercial and/or recreationalaircrafts, instrument flight aircrafts, drones, electric aircrafts,airliners, rotorcrafts, vertical takeoff and landing aircrafts, jets,airships, blimps, gliders, paramotors, and the like. Aircraft 100 mayinclude an electrically powered aircraft. In embodiments, electricallypowered aircraft may be an electric vertical takeoff and landing (eVTOL)aircraft. Electric aircraft may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane-style landing, and/or anycombination thereof. Electric aircraft may include one or more mannedand/or unmanned aircrafts. Electric aircraft may include one or moreall-electric short takeoff and landing (eSTOL) aircrafts. For example,and without limitation, eSTOL aircrafts may accelerate plane to a flightspeed on takeoff and decelerate plane after landing. In an embodiment,and without limitation, electric aircraft may be configured with anelectric propulsion assembly. Electric propulsion assembly may includeany electric propulsion assembly as described in U.S. Nonprovisionalapplication Ser. No. 16/603,225, filed on Dec. 4, 2019, and entitled “ANINTEGRATED ELECTRIC PROPULSION ASSEMBLY,” the entirety of which isincorporated herein by reference.

Still referring to FIG. 1 , aircraft 100, may include a fuselage 104, aflight component 108 (or one or more flight components 108), and sensor112. Sensor 112 is described further herein with reference to FIG. 2 .

As used in this disclosure, a vertical take-off and landing (VTOL)aircraft is an aircraft that can hover, take off, and land vertically.An eVTOL, as used in this disclosure, is an electrically poweredaircraft typically using an energy source, of a plurality of energysources to power aircraft. To optimize power and energy necessary topropel aircraft 100, eVTOL may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane style landing, and/or anycombination thereof. Rotor-based flight, as described herein, is whereaircraft generates lift and propulsion by way of one or more poweredrotors or blades coupled with an engine, such as a “quad-copter,”multi-rotor helicopter, or other vehicle that maintains its liftprimarily using downward thrusting propulsors. “Fixed-wing flight”, asdescribed herein, is where aircraft is capable of flight using wingsand/or foils that generate lift caused by the aircraft's forwardairspeed and the shape of the wings and/or foils, such as airplane-styleflight.

Still referring to FIG. 1 , as used in this disclosure a “fuselage” is amain body of an aircraft, or in other words, the entirety of theaircraft except for a cockpit, nose, wings, empennage, nacelles, any andall control surfaces, and generally contains an aircraft's payload.Fuselage 104 may include structural elements that physically support ashape and structure of an aircraft. Structural elements may take aplurality of forms, alone or in combination with other types. Structuralelements may vary depending on a construction type of aircraft such aswithout limitation a fuselage 104. Fuselage 104 may include a trussstructure. A truss structure may be used with a lightweight aircraft andincludes welded steel tube trusses. A “truss,” as used in thisdisclosure, is an assembly of beams that create a rigid structure, oftenin combinations of triangles to create three-dimensional shapes. A trussstructure may alternatively include wood construction in place of steeltubes, or a combination thereof. In embodiments, structural elements mayinclude steel tubes and/or wood beams. In an embodiment, and withoutlimitation, structural elements may include an aircraft skin. Aircraftskin may be layered over the body shape constructed by trusses. Aircraftskin may include a plurality of materials such as plywood sheets,aluminum, fiberglass, and/or carbon fiber, the latter of which will beaddressed in greater detail later herein.

In embodiments, and with continued reference to FIG. 1 , aircraftfuselage 104 may include and/or be constructed using geodesicconstruction. Geodesic structural elements may include stringers woundabout formers (which may be alternatively called station frames) inopposing spiral directions. A “stringer,” as used in this disclosure, isa general structural element that may include a long, thin, and rigidstrip of metal or wood that is mechanically coupled to and spans adistance from, station frame to station frame to create an internalskeleton on which to mechanically couple aircraft skin. A former (orstation frame) may include a rigid structural element that is disposedalong a length of an interior of aircraft fuselage 104 orthogonal to alongitudinal (nose to tail) axis of the aircraft and may form a generalshape of fuselage 104. A former may include differing cross-sectionalshapes at differing locations along fuselage 104, as the former is thestructural element that informs the overall shape of a fuselage 104curvature. In embodiments, aircraft skin may be anchored to formers andstrings such that the outer mold line of a volume encapsulated byformers and stringers may include the same shape as aircraft 100 wheninstalled. In other words, former(s) may form a fuselage's ribs, and thestringers may form the interstitials between such ribs. The spiralorientation of stringers about formers may provide uniform robustness atany point on an aircraft fuselage such that if a portion sustainsdamage, another portion may remain largely unaffected. Aircraft skin maybe attached to underlying stringers and formers and may interact with afluid, such as air, to generate lift and perform maneuvers.

In an embodiment, and still referring to FIG. 1 , fuselage 104 mayinclude and/or be constructed using monocoque construction. Monocoqueconstruction may include a primary structure that forms a shell (or skinin an aircraft's case) and supports physical loads. Monocoque fuselagesare fuselages in which the aircraft skin or shell is also the primarystructure. In monocoque construction aircraft skin would support tensileand compressive loads within itself and true monocoque aircraft can befurther characterized by the absence of internal structural elements.Aircraft skin in this construction method is rigid and can sustain itsshape with no structural assistance form underlying skeleton-likeelements. Monocoque fuselage may include aircraft skin made from plywoodlayered in varying grain directions, epoxy-impregnated fiberglass,carbon fiber, or any combination thereof.

According to embodiments, and further referring to FIG. 1 , fuselage 104may include a semi-monocoque construction. Semi-monocoque construction,as used herein, is a partial monocoque construction, wherein a monocoqueconstruction is describe above detail. In semi-monocoque construction,aircraft fuselage 104 may derive some structural support from stressedaircraft skin and some structural support from underlying framestructure made of structural elements. Formers or station frames can beseen running transverse to the long axis of fuselage 104 with circularcutouts which are generally used in real-world manufacturing for weightsavings and for the routing of electrical harnesses and other modernon-board systems. In a semi-monocoque construction, stringers are thin,long strips of material that run parallel to fuselage's long axis.Stringers may be mechanically coupled to formers permanently, such aswith rivets. Aircraft skin may be mechanically coupled to stringers andformers permanently, such as by rivets as well. A person of ordinaryskill in the art will appreciate, upon reviewing the entirety of thisdisclosure, that there are numerous methods for mechanical fastening ofcomponents like screws, nails, dowels, pins, anchors, adhesives likeglue or epoxy, or bolts and nuts, to name a few. A subset of fuselageunder the umbrella of semi-monocoque construction may include unibodyvehicles. Unibody, which is short for “unitized body” or alternatively“unitary construction”, vehicles are characterized by a construction inwhich the body, floor plan, and chassis form a single structure. In theaircraft world, unibody may be characterized by internal structuralelements like formers and stringers being constructed in one piece,integral to the aircraft skin as well as any floor construction like adeck.

Still referring to FIG. 1 , stringers and formers, which may account forthe bulk of an aircraft structure excluding monocoque construction, maybe arranged in a plurality of orientations depending on aircraftoperation and materials. Stringers may be arranged to carry axial(tensile or compressive), shear, bending or torsion forces throughouttheir overall structure. Due to their coupling to aircraft skin,aerodynamic forces exerted on aircraft skin will be transferred tostringers. A location of said stringers greatly informs the type offorces and loads applied to each and every stringer, all of which may behandled by material selection, cross-sectional area, and mechanicalcoupling methods of each member. A similar assessment may be made forformers. In general, formers may be significantly larger incross-sectional area and thickness, depending on location, thanstringers. Both stringers and formers may include aluminum, aluminumalloys, graphite epoxy composite, steel alloys, titanium, or anundisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 1 , stressed skin, whenused in semi-monocoque construction is the concept where the skin of anaircraft bears partial, yet significant, load in an overall structuralhierarchy. In other words, an internal structure, whether it be a frameof welded tubes, formers and stringers, or some combination, may not besufficiently strong enough by design to bear all loads. The concept ofstressed skin may be applied in monocoque and semi-monocoqueconstruction methods of fuselage 104. Monocoque may include onlystructural skin, and in that sense, aircraft skin undergoes stress byapplied aerodynamic fluids imparted by the fluid. Stress as used incontinuum mechanics may be described in pound-force per square inch(lbf/in²) or Pascals (Pa). In semi-monocoque construction stressed skinmay bear part of aerodynamic loads and additionally may impart force onan underlying structure of stringers and formers.

Still referring to FIG. 1 , it should be noted that an illustrativeembodiment is presented only, and this disclosure in no way limits theform or construction method of a system and method for loading payloadinto an eVTOL aircraft. In embodiments, fuselage 104 may be configurablebased on the needs of the eVTOL per specific mission or objective. Thegeneral arrangement of components, structural elements, and hardwareassociated with storing and/or moving a payload may be added or removedfrom fuselage 104 as needed, whether it is stowed manually, automatedly,or removed by personnel altogether. Fuselage 104 may be configurable fora plurality of storage options. Bulkheads and dividers may be installedand uninstalled as needed, as well as longitudinal dividers wherenecessary. Bulkheads and dividers may be installed using integratedslots and hooks, tabs, boss and channel, or hardware like bolts, nuts,screws, nails, clips, pins, and/or dowels, to name a few. Fuselage 104may also be configurable to accept certain specific cargo containers, ora receptable that can, in turn, accept certain cargo containers.

Still referring to FIG. 1 , aircraft 100 may include a plurality oflaterally extending elements attached to fuselage 104. As used in thisdisclosure a “laterally extending element” is an element that projectsessentially horizontally from fuselage, including an outrigger, a spar,and/or a fixed wing that extends from fuselage. Wings may be structureswhich may include airfoils configured to create a pressure differentialresulting in lift. Wings may generally dispose on the left and rightsides of the aircraft symmetrically, at a point between nose andempennage. Wings may include a plurality of geometries in planform view,swept swing, tapered, variable wing, triangular, oblong, elliptical,square, among others. A wing's cross section geometry may include anairfoil. An “airfoil” as used in this disclosure is a shape specificallydesigned such that a fluid flowing above and below it exert differinglevels of pressure against the top and bottom surface. In embodiments,the bottom surface of an aircraft can be configured to generate agreater pressure than does the top, resulting in lift. Laterallyextending element may include differing and/or similar cross-sectionalgeometries over its cord length or the length from wing tip to wherewing meets aircraft's body. One or more wings may be symmetrical aboutaircraft's longitudinal plane, which includes the longitudinal or rollaxis reaching down the center of aircraft through the nose andempennage, and plane's yaw axis. Laterally extending element may includecontrols surfaces configured to be commanded by a pilot or pilots tochange a wing's geometry and therefore its interaction with a fluidmedium, like air. Control surfaces may include flaps, ailerons, tabs,spoilers, and slats, among others. The control surfaces may dispose onthe wings in a plurality of locations and arrangements and inembodiments may be disposed at the leading and trailing edges of thewings, and may be configured to deflect up, down, forward, aft, or acombination thereof. An aircraft, including a dual-mode aircraft mayinclude a combination of control surfaces to perform maneuvers whileflying or on ground.

Still referring to FIG. 1 , aircraft 100 may include a plurality offlight components 108. As used in this disclosure a “flight component”is a component that promotes flight and guidance of an aircraft. In anembodiment, flight component 108 may be mechanically coupled to anaircraft. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling mayinclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling may be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling may be used to join two pieces ofrotating electric aircraft components.

Still referring to FIG. 1 , plurality of flight components 108 mayinclude at least a lift propulsor. As used in this disclosure a“propulsor” is a component and/or device used to propel a craft upwardby exerting force on a fluid medium, which may include a gaseous mediumsuch as air or a liquid medium such as water. Propulsor may include anydevice or component that consumes electrical power on demand to propelan electric aircraft in a direction or other vehicle while on ground orin-flight. For example, and without limitation, propulsor may include arotor, propeller, paddle wheel and the like thereof. In an embodiment,propulsor may include a plurality of blades. As used in this disclosurea “blade” is a propeller that converts rotary motion from an engine orother power source into a swirling slipstream. In an embodiment, blademay convert rotary motion to push the propeller forwards or backwards.In an embodiment propulsor may include a rotating power-driven hub, towhich are attached several radial airfoil-section blades such that thewhole assembly rotates about a longitudinal axis. The lift propulsor isfurther described herein with reference to FIG. 2 .

In an embodiment, and still referring to FIG. 1 , plurality of flightcomponents 108 may include one or more power sources. As used in thisdisclosure a “power source” is a source that that drives and/or controlsany other flight component. For example, and without limitation powersource may include a motor that operates to move one or more liftpropulsor components, to drive one or more blades, or the like thereof.A motor may be driven by direct current (DC) electric power and mayinclude, without limitation, brushless DC electric motors, switchedreluctance motors, induction motors, or any combination thereof. A motormay also include electronic speed controllers or other components forregulating motor speed, rotation direction, and/or dynamic braking. Inan embodiment, power source may include an inverter. As used in thisdisclosure an “inverter” is a device that changes one or more currentsof a system. For example, and without limitation, inverter may includeone or more electronic devices that change direct current to alternatingcurrent. As a further non-limiting example, inverter may includereceiving a first input voltage and outputting a second voltage, whereinthe second voltage is different from the first voltage. In anembodiment, and without limitation, inverter may output a waveform,wherein a waveform may include a square wave, sine wave, modified sinewave, near sine wave, and the like thereof.

Still referring to FIG. 1 , power source may include an energy source.An energy source may include, for example, a generator, a photovoltaicdevice, a fuel cell such as a hydrogen fuel cell, direct methanol fuelcell, and/or solid oxide fuel cell, an electric energy storage device(e.g. a capacitor, an inductor, and/or a battery). An energy source mayalso include a battery cell, or a plurality of battery cells connectedin series into a module and each module connected in series or inparallel with other modules. Configuration of an energy sourcecontaining connected modules may be designed to meet an energy or powerrequirement and may be designed to fit within a designated footprint inan electric aircraft in which aircraft 100 may be incorporated.

In an embodiment, and still referring to FIG. 1 , an energy source maybe used to provide a steady supply of electrical power to a load overthe course of a flight by a vehicle or other electric aircraft. Forexample, the energy source may be capable of providing sufficient powerfor “cruising” and other relatively low-energy phases of flight. Anenergy source may also be capable of providing electrical power for somehigher-power phases of flight as well, particularly when the energysource is at a high SOC, as may be the case for instance during takeoff.In an embodiment, the energy source may be capable of providingsufficient electrical power for auxiliary loads including withoutlimitation, lighting, navigation, communications, de-icing, steering orother systems requiring power or energy. Further, the energy source maybe capable of providing sufficient power for controlled descent andlanding protocols, including, without limitation, hovering descent orrunway landing. As used herein the energy source may have high powerdensity where the electrical power an energy source can usefully produceper unit of volume and/or mass is relatively high. The electrical poweris defined as the rate of electrical energy per unit time. An energysource may include a device for which power that may be produced perunit of volume and/or mass has been optimized, at the expense of themaximal total specific energy density or power capacity, during design.Non-limiting examples of items that may be used as at least an energysource may include batteries used for starting applications including Liion batteries which may include NCA, NMC, Lithium iron phosphate(LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may bemixed with another cathode chemistry to provide more specific power ifthe application requires Li metal batteries, which have a lithium metalanode that provides high power on demand, Li ion batteries that have asilicon or titanite anode, energy source may be used, in an embodiment,to provide electrical power to an electric aircraft or drone, such as anelectric aircraft vehicle, during moments requiring high rates of poweroutput, including without limitation takeoff, landing, thermal de-icingand situations requiring greater power output for reasons of stability,such as high turbulence situations, as described in further detailbelow. A battery may include, without limitation a battery using nickelbased chemistries such as nickel cadmium or nickel metal hydride, abattery using lithium ion battery chemistries such as a nickel cobaltaluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate(LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide(LMO), a battery using lithium polymer technology, lead-based batteriessuch as without limitation lead acid batteries, metal-air batteries, orany other suitable battery. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 1 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Themodule may include batteries connected in parallel or in series or aplurality of modules connected either in series or in parallel designedto deliver both the power and energy requirements of the application.Connecting batteries in series may increase the voltage of at least anenergy source which may provide more power on demand. High voltagebatteries may require cell matching when high peak load is needed. Asmore cells are connected in strings, there may exist the possibility ofone cell failing which may increase resistance in the module and reducethe overall power output as the voltage of the module may decrease as aresult of that failing cell. Connecting batteries in parallel mayincrease total current capacity by decreasing total resistance, and italso may increase overall amp-hour capacity. The overall energy andpower outputs of at least an energy source may be based on theindividual battery cell performance or an extrapolation based on themeasurement of at least an electrical parameter. In an embodiment wherethe energy source includes a plurality of battery cells, the overallpower output capacity may be dependent on the electrical parameters ofeach individual cell. If one cell experiences high self-discharge duringdemand, power drawn from at least an energy source may be decreased toavoid damage to the weakest cell. The energy source may further include,without limitation, wiring, conduit, housing, cooling system and batterymanagement system. Persons skilled in the art will be aware, afterreviewing the entirety of this disclosure, of many different componentsof an energy source.

Still referring to FIG. 1 , plurality of flight components 108 mayinclude a pusher component. As used in this disclosure a “pushercomponent” is a component that pushes and/or thrusts an aircraft througha medium. As a non-limiting example, pusher component may include apusher propeller, a paddle wheel, a pusher motor, a pusher propulsor,and the like. Additionally, or alternatively, pusher flight componentmay include a plurality of pusher flight components. Pusher componentmay be configured to produce a forward thrust. As used in thisdisclosure a “forward thrust” is a thrust that forces aircraft through amedium in a horizontal direction, wherein a horizontal direction is adirection parallel to the longitudinal axis. For example, forward thrustmay include a force of 1145 N to force aircraft to in a horizontaldirection along the longitudinal axis. As a further non-limitingexample, pusher component may twist and/or rotate to pull air behind itand, at the same time, push aircraft 100 forward with an equal amount offorce. In an embodiment, and without limitation, the more air forcedbehind aircraft, the greater the thrust force with which aircraft 100 ispushed horizontally will be. In another embodiment, and withoutlimitation, forward thrust may force aircraft 100 through the medium ofrelative air. Additionally or alternatively, plurality of flightcomponents 108 may include one or more puller components. As used inthis disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

Still referring to FIG. 1 , aircraft 100 may have a computing deviceattached. Computing device could include but is not limited to a flightcontroller, simulation device, and the like. As used in this disclosurea “flight controller” is a computing device of a plurality of computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and flight instruction. Computing device may includeand/or communicate with any other computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, computing device mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, computing device may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith. In an embodiment, and without limitation, computing devicemay be configured to command a plurality of flight components, whereinflight components are described in reference to FIG. 1 . Flightcontroller is described herein more detail in reference to FIG. 3 .

Still referring to FIG. 1 , aircraft 100 includes sensor 112. As used inthis disclosure a “sensor” is a device, module, and/or subsystem,utilizing any hardware, software, and/or any combination thereof todetect events and/or changes in the instant environment and transmit theinformation. Sensor 112 may be attached via a mechanically and/orcommunicatively coupled, as described above, to aircraft. For example,and without limitation, sensor 112 may include a potentiometric sensor,inductive sensor, capacitive sensor, piezoelectric sensor, strain gaugesensor, variable reluctance sensor, and the like thereof. Sensor 112 mayinclude one or more environmental sensors, which may function to senseparameters of the environment surrounding the aircraft. An environmentalsensor may include without limitation one or more sensors used to detectambient temperature, barometric pressure, and/or air velocity, one ormore motion sensors which may include without limitation gyroscopes,accelerometers, inertial measurement unit (IMU), and/or magneticsensors, one or more humidity sensors, one or more oxygen sensors, orthe like. Additionally or alternatively, sensor 112 may include ageospatial sensor. Sensor 112 may be located outside the aircraft;and/or be included in and/or attached to at least a portion of theaircraft. Sensor 112 may include one or more proximity sensors,displacement sensors, vibration sensors, and the like thereof. Sensor112 may be used to monitor the status of aircraft for both critical andnon-critical functions. Sensor 112 may be incorporated into vehicle oraircraft or be remote. Sensor 112 is described further herein withreference to FIG. 2A.

Now referring to FIG. 2A, system 200 illustrates a side view ofcross-sectional area of boundary 204 associated with thethree-dimensional flying space. Cross-sectional area of the boundary ofthe flying space may be an ellipse shape, like as shown, but could alsobe a rectangular shape, circular shape, or any shape therewith. System200 includes aircraft 100, sensor 112, and boundary 204. As used in thisdisclosure, “boundary” is an invisible fence that limits the area thataircraft can fly in. As used in this disclosure, “three-dimensionalflying space” is an area in which aircraft is allowed to fly in, eitherin real life or a flying simulation. Boundary 204 confines aircraft 100to a specified flying area within the three-dimensional flying space.Boundary 204 is described further below.

Still referring to FIG. 2A, aircraft 100 includes sensor 112 may includean optical sensor. As used in this disclosure an “optical sensor” is anelectronic device that alters any parameter of an electronic circuitwhen contacted by visible or NIR light. Optical detectors may include,without limitation, charge-coupled devices (CCD), photodiodes, avalanchephotodiodes (APDs), silicon photo-multipliers (SiPMs), complementarymetal-oxide-semiconductor (CMOS), scientific CMOS (sCMOS), micro-channelplates (MCPs), micro-channel plate photomultiplier tubes (MCP-PMTs),single photon avalanche diode (SPAD), Electron Bombarded Active PixelSensor (EBAPS), quanta image sensor (QIS), spatial phase imagers (SPI),quantum dot cameras, image intensification tubes, photovoltaic imagers,optical flow sensors and/or imagers, photoresistors and/orphotosensitive or photon-detecting circuit elements, semiconductorsand/or transducers. APDs, as used herein, are diodes (e.g. withoutlimitation p-n, p-i-n, and others) reverse biased such that a singlephoton generated carrier can trigger a short, temporary “avalanche” ofphotocurrent on the order of milliamps or more caused by electrons beingaccelerated through a high field region of the diode and impact ionizingcovalent bonds in the bulk material, these in turn triggering greaterimpact ionization of electron-hole pairs. APDs may provide a built-instage of gain through avalanche multiplication. When a reverse bias isless than breakdown voltage, a gain of an APD may be approximatelylinear. For silicon APDs this gain may be on the order of 10-100.Material of the APD may contribute to gains.

Still referring to FIG. 2A, optical sensor may be configured to identifya topographical datum. As used in this disclosure a “topographicaldatum” is an element of datum representing the arrangement and/orlocation of a physical feature of a geolocation. For example, andwithout limitation, topographical datum may include one or more elementsof datum denoting a mountain range, skyscraper, river, ridge, ocean,lake, vehicle, animal, person, street, field, tree, and the likethereof. In an embodiment, and without limitation, optical sensor mayinclude a light radar component. As used in this disclosure a “lightradar component” is an active imaging source that transmits light towardan object or field of interest and detects back-scattered or reflectedlight, measuring time of flight (ToF), interferometry, and/or phase ofsuch back-scattered and/or reflected light to compute distances to,velocities, and/or accelerations of objects at points from whichback-scatter and/or reflection occurred. In an embodiment, thewavelength of light may be outside the range of visible light; forinstance, and without limitation, wavelength may be in the infraredrange as described above. Light radar component may include a “flashlidar” component, mechanical or non-mechanical beam steering, lightpatterns, and/or computational imaging methods, such as plenoptic orother multi-aperture embodiments. In an embodiment, and withoutlimitation, light radar component may include one or more opticalelements for focusing, collimating, and/or transmitting light emitted bylight source. In an embodiment, intensity and/or focus may default tominimally harmful settings, permitting allowing ToF ranging or the liketo determine a distance to a nearest topographical data point and/orground point. Light radar component may include detectors that may besensitive specifically to a narrow band of wavelengths transmitted bylight source, and/or may be sensitive to a range of wavelengths thatincludes the band transmitted by the light source. Detectors may bedesigned to react quickly to initial detection of photons, for instancethrough use of APDs or other highly sensitive detectors.

In an embodiment, and still referring to FIG. 2A, optical sensor may beconfigured to calculate an in-air position as a function of thetopographical datum. As used in this disclosure an “in-air position” isa relative location and/or orientation of an aircraft relative to thetopographical datum. For example, and without limitation, optical sensormay perform a ToF calculation as a function of the one or more lightradar components by firing pulses of light and measuring time requiredfor a backscattered and/or reflected pulse to return. As a furthernon-limiting example, ToF may be used to measure a distance from lightradar component to a point from which light is scattered; this may beused, without limitation, to detect distance to a topographical datumsuch as a building. Distance may be computed using a single reading ofToF, by averaging two or more ToF readings, and/or measuring multiplereturns to reduce false readings from clutter. ToF may be used to detectedges of objects such as an edge of a cliff. ToF may be used to generatean image, for instance by repeatedly capturing readings of ToF todifferent portions of an object and/or topographical datum; athree-dimensional surface contour of the object, such as facialfeatures, details of an object a person is holding, or the like, may berendered using ToF data. ToF measurements may be processed to generate adepth map or point cloud, defined for purposes of this disclosure as aset of Z-coordinate values for every pixel of the image, which may bemeasured in units of millimeters, micrometers, or the like. Depth mapdata may be combined with other imaging data; for instance, intensity orphase values of pixels in an infrared reading may be measured asproportional to an amount of light returned from a scene.

Still referring to FIG. 2A, sensor 112 may include a ranging sensor. Asused in this disclosure, a “ranging sensor” is an electronic device thatreceives, stores, and/or transmits one or more elements of spatialinformation. For example, and without limitation, ranging sensor mayreceive a temporal indicator. As used in this disclosure, a “temporalindicator” is an element of datum denoting a time and/or temporalelement. For example, and without limitation, temporal indicator mayinclude a time period, wherein a time period is a magnitude of timeelapsed, such as but not limited to seconds, minutes, hours, days,weeks, months, years, and the like thereof. For example, and withoutlimitation, temporal indicator may denote a time period that aircrafthas been in flight and/or traveling in a medium, such as but not limitedto air. As a further non-limiting example, temporal indicator may denotea time period that aircraft has been idling and/or stationary. As afurther non-limiting example, temporal indicator may denote a timeperiod that aircraft has been at a cruising altitude. As a furthernon-limiting example, temporal indicator may denote a time period thataircraft has been climbing and/or descending from a cruising altitude.As a further non-limiting example, temporal indicator may denote a timeperiod that a motor has been expending energy. As a further non-limitingexample, temporal indicator may denote a time period that a torqueand/or thrust has been exerted by a flight component, wherein a flightcomponent is described above in detail.

In an embodiment, and still referring to FIG. 2A, ranging sensor may beconfigured to calculate a distance as a function of the temporalindicator and the boundary. As used in this disclosure a “distance” is ameasurement of travel and/or progress that has progressed. For example,and without limitation distance may denote a number of kilometers and/ormiles that have been traveled. As a further non-limiting example,distance may denote a progression of distance traveled as a function ofa required distance to be traveled. In an embodiment, distance maydenote one or more replacement points. As used in this disclosure a“replacement point” is a distance and/or progression interval in which acomponent and/or aircraft has deteriorated. For example, and withoutlimitation, replacement point may denote that an aircraft has 1200 kmremaining prior to requiring maintenance. As a further non-limitingexample, replacement point may denote that a flight component has 5%remaining prior to requiring a replacement component.

Still referring to FIG. 2A, sensor 112 detects an aircraft position. Inthis disclosure, “aircraft position” is the location of aircraft interms of longitude and latitude. Data that contains the aircraftposition is called aircraft location datum and is further describedbelow in reference to FIG. 3 .

Still referring to FIG. 2A, sensor 112 detects boundary informationassociated with the three-dimensional flying space. “Boundaryinformation” contains the information concerning the exact location ofthe closest point on boundary 204 that is preprogrammed into the sensor.Data that contains the boundary information is called the boundary datumand is further described below in reference to FIG. 3 .

Now referring to FIG. 2B, system 200 illustrates a side view ofcross-sectional area of boundary 204 associated with thethree-dimensional flying space. Cross-sectional area of the boundary ofthe flying space may be an ellipse shape, like as shown, but could alsobe a rectangular shape, circular shape, or any shape therewith. System200 includes aircraft 100, sensor 112, and boundary 204. All three arediscussed further below in reference to FIG. 3 . Everything as describedbefore in reference to FIG. 2A is applicable to FIG. 2B as well.

Now referring to FIG. 3 , system 300 illustrates an exemplary embodimentof a system for determining boundaries in a simulation of an electricaircraft. System includes sensor 112, aircraft location datum 304,boundary datum 308, computing device 116, distance datum 312,recommended aircraft adjustment 316, and remote device 320.

Still referring to FIG. 3 , system 300 includes a sensor 112 configuredto detect an aircraft location datum 304 and boundary datum 308. As saidearlier, “aircraft location datum” includes of information concerningaircraft's position. Aircraft location datum 304 may include a longitudedatum and a latitude datum. For example, but without limitation,aircraft position could be latitude: 38.42964, longitude: 123.41508, orthe like. As used in this disclosure, “boundary datum” contains positioninformation about a single point on boundary, out of the plurality ofinfinite points that include boundary, that is closest to aircraftlocation datum. Boundary datum 308 is configured to include a longitudedatum and a latitude datum that are pre-programmed into the sensor torepresent the 3D flying space limit. An example, but without limitation,of a boundary datum may be latitude: 43.56724, longitude: 80.90365,which is programmed in the sensor to be located in on boundary and isthe closest point on that boundary to whatever the longitude andlatitude of aircraft position are.

Continuously referring to FIG. 3 , sensor 112 is configured to transmitaircraft location datum 304 and boundary datum 308 to computing device116. Computing device 116 may be a flight controller, tablet, smartphone, computer, or the like and is further described herein withreference to FIG. 7 .

Still referring to FIG. 3 , system 300 includes a computing device 116configured to receive the datums from the sensor and determine adistance datum 312 as a function of transmit aircraft location datum 304and boundary datum 308. In this disclosure, “distance datum” is thedistance between aircraft location datum 304 and boundary datum 308.Distance datum 312 may be any length and measured in any type of metricsystem. Examples of distance datum 312 include, without limitation, 654yards, 4.3 kilometers, 392 centimeters, or the like. See abovedescription herein with reference to FIG. 2A, to see examples of howdistance can be detected. Distance datum could also be just thesubtraction difference between the longitude and latitude datums ofaircraft location datum 304 and boundary datum 308.

Still referring to FIG. 3 , computing device 116 is also configured togenerate a recommended aircraft adjustment 316 as a function of distancedatum 312. As used in this disclosure, “recommended aircraft adjustment”is an action of movement of an aircraft that is suggested to pilot fromcomputing device. For example, but without limitation, computing devicegenerate a recommendation of a 180-degree turn if the plane is travelingthe wrong way. Other examples of aircraft adjustments may includestraight-and-level flight, climbs, descents, lazy eights, turns, and thelike. Aircraft adjustments may recommend pilot to follow a particularflight path, what to do next, where to move controls and the like. Inthis disclosure, “recommended adjustment” may refer to a specific actionof flight for the pilot/autopilot to execute. For instance, but withoutlimitation, examples of flight adjustments may includestraight-and-level flight, turns, climbs, and descents. Recommendedaircraft adjustment 316 also could just be to tell pilot to move awayfarther away from boundary.

Continuously referring to FIG. 3 , computing device 116 transmitsdistance datum 312 and recommended aircraft adjustment 316 to a remotedevice 320. As used in this disclosure, “remote device” is any visualpresentation of data. For example, but without limitation, distancedatum 312 and recommended aircraft adjustment 316 may be shown through asort of computer screen, VR goggles, a tablet, a phone, a gaming deviceor the like. Other examples may include various types of displaysincluding but not limited to electroluminescent display (ELD), a liquidcrystal display (LCD), a light-emitting diode (LED), a plasma display(PDP), and/or a quantum dot display (QLED). Other displays may include ahead mounted display, a head-up display, a display incorporated ineyeglasses, googles, headsets, helmet display systems, or the like, adisplay incorporated in contact lenses, an eye tap display systemincluding without limitation a laser eye tap device, VRD, or the like.When developing remote device 320, it is important to keep in mind thatremote device 320 may need to be easily visually accessible by pilot.Remote device 320 may be part of sensor 112 or computing device 116 orbe a completely separate entity in aircraft. Remote device 320 may alsobe a stereoscopic display, which may denote a display that simulates auser experience of viewing a three-dimensional space and/or object, forinstance by simulating and/or replicating different perspectives of auser's two eyes; this is in contrast to a two-dimensional image, inwhich images presented to each eye are substantially identical, such asmay occur when viewing a flat screen display. Stereoscopic display maydisplay two flat images having different perspectives, each to only oneeye, which may simulate the appearance of an object or space as seenfrom the perspective of that eye. Alternatively or additionally,stereoscopic display may include a three-dimensional display such as aholographic display or the like. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousoptical projection and/or remote device 320 technologies that may beincorporated in system 300 consistently with this disclosure.

Still referring to FIG. 3 , computing device 320 may use a mesh network.Disclosure related to use of mesh network to communicate betweencomputing device and aircraft includes U.S. patent application Ser. No.17/348,916 entitled “METHODS AND SYSTEMS FOR SIMULATED OPERATION OF ANELECTRIC VERTICAL TAKE-OFF AND LANDING (EVTOL) AIRCRAFT,” incorporatedherein by reference in its entirety.

Still referring to FIG. 3 , remote device 320 receives datums fromcomputing device 116 and displays distance datum 312 and recommendedaircraft adjustment 316 to a user. Remote device 320 may notify the userof distance datum 312 and aircraft adjustment 316 through a visual andauditory alert. Examples of this are, but without limitation, a bell, aringing noise, a flashing light, a change in color, or the like. Remotedevice also presents a speed limit associated with the three-dimensionflying space. In this disclosure, “speed limit” is the maximum speed onecan travel in a specific area, so inside the 3D flying space. Pilot willalso be alerted if the speed limit has been exceeded. Notifications ofthis may be the same as above. Remote device 320 also alerts user ifdistance datum is below a distance threshold. In this disclosure,“distance threshold” is a specified distance between aircraft locationand boundary; pilot may be notified if aircraft is too close toboundary. For example but without limitation, a red light near pilotmight turn on if aircraft is within 100 feet of boundary.

Still referring to FIG. 3 , remote device 320 is further configured toreceive and transmit user interactions to the computing device. In thisdisclosure, a “user interaction” is when someone interacts with userinterface, or in the case, remote device 320. Examples of userinteractions can be, but are not limited to, silencing the alert,stopping flashing of lights, changing distance threshold, and the like.Computing device then receives user interaction and generates an updatedrecommended aircraft adjustment to be transmitted back to remote device320. In this disclosure, “updated recommended aircraft adjustment” isaircraft adjustment after user has interacted with remote device 320.Further, a “user” in this disclosure is anyone interacting with theflight controls of the aircraft. Examples include but are not limited toa pilot, co-pilot, a pilot in a remote location, or a pilot simulator.

Now referring to FIG. 4 , an exemplary embodiment 400 of a possiblecomputing device 116, a flight controller, is illustrated. As used inthis disclosure a “flight controller” is a computing device of aplurality of computing devices dedicated to data storage, security,distribution of traffic for load balancing, and flight instruction.Flight controller may include and/or communicate with any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Further, flightcontroller may include a single computing device operatingindependently, or may include two or more computing device operating inconcert, in parallel, sequentially or the like; two or more computingdevices may be included together in a single computing device or in twoor more computing devices. In embodiments, flight controller may beinstalled in an aircraft, may control aircraft remotely, and/or mayinclude an element installed in the aircraft and a remote element incommunication therewith.

In an embodiment, and still referring to FIG. 4 , flight controller mayinclude a signal transformation component 404. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 404 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component404 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 404 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 404 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 404 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 4 , signal transformation component 404 may beconfigured to optimize an intermediate representation 408. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 404 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 404 may optimizeintermediate representation 408 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 404 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 404 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 116. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 404 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 4 , flight controller mayinclude a reconfigurable hardware platform 412. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 412 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 4 , reconfigurable hardware platform 412 mayinclude a logic component 416. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 416 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 416 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 416 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating-point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 416 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 416 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 408. Logiccomponent 416 may be configured to fetch and/or retrieve instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller. Logiccomponent 416 may be configured to decode instruction retrieved frommemory cache to opcodes and/or operands. Logic component 416 may beconfigured to execute instruction on intermediate representation 408and/or output language. For example, and without limitation, logiccomponent 416 may be configured to execute an addition operation onintermediate representation 408 and/or output language.

In an embodiment, and without limitation, logic component 416 may beconfigured to calculate a flight element 420. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 420 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 420 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 420 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 4 , flight controller may include a chipsetcomponent 424. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 424 may include a northbridge data flowpath, wherein northbridge dataflow path may manage data flow from logiccomponent 416 to a high-speed device and/or component, such as a RAM,graphics controller, and the like thereof. In another embodiment, andwithout limitation, chipset component 424 may include a southbridge dataflow path, wherein southbridge dataflow path may manage data flow fromlogic component 416 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 424 maymanage data flow between logic component 416, memory cache, and a flightcomponent 108. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 108 may include acomponent used to affect aircrafts' roll and pitch which may include oneor more ailerons. As a further example, flight component 108 may includea rudder to control yaw of an aircraft. In an embodiment, chipsetcomponent 424 may be configured to communicate with a plurality offlight components as a function of flight element 420. For example, andwithout limitation, chipset component 424 may transmit to an aircraftrotor to reduce torque of a first lift propulsor and increase theforward thrust produced by a pusher component to perform a flightmaneuver.

In an embodiment, and still referring to FIG. 4 , flight controller isconfigured generate an autonomous function. As used in this disclosurean “autonomous function” is a mode and/or function of flight controllerthat controls aircraft automatically. For example, and withoutlimitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 420. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller will adjustaircraft. As used in this disclosure a “semi-autonomous mode” is a modethat automatically adjusts and/or controls a portion and/or section ofaircraft.

In an embodiment, and still referring to FIG. 4 , flight controllergenerates autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 420 and pilot override428 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot override switch” is an element of datum representing one or morefunctions a pilot does to claim flight control of aircraft 100. Forexample, pilot override 428 may denote that a pilot is gaining controland/or maneuvering ailerons, rudders and/or propulsors. In anembodiment, pilot override 428 must include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot override 428may include an explicit signal, wherein pilot explicitly states desirefor control. As a further non-limiting example, pilot override 428 mayinclude an explicit signal directing flight controller to control and/ormaintain entire aircraft, and/or entire flight plan. As a furthernon-limiting example, pilot override 428 may include an implicit signal,wherein flight controller detects a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot override 428 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot override 428 may include oneor more local and/or global signals. For example, and withoutlimitation, pilot override 428 may include a local signal that istransmitted by a pilot and/or crew member. As a further non-limitingexample, pilot override 428 may include a global signal that istransmitted by air traffic control and/or one or more remote users thatare in communication with pilot of aircraft.

Still referring to FIG. 4 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure “remotedevice” is an external device to flight controller. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elastic net regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 4 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that may include a semi-autonomousmode to increase thrust of propulsors. Autonomous training data may bereceived as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot override,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot override, and/or simulation datato an autonomous function.

Still referring to FIG. 4 , flight controller may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor, and the like thereof.Remote device and/or FPGA may perform autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit output to flight controller. Remote device and/or FPGA maytransmit a signal, bit, datum, or parameter to flight controller that atleast relates to autonomous function. Additionally or alternatively,remote device and/or FPGA may provide an updated machine-learning model.For example, and without limitation, an updated machine-learning modelmay be included of a firmware update, a software update, an autonomousmachine-learning process correction, and the like thereof As anon-limiting example, a software update may incorporate a new simulationdata that relates to a modified flight element. Additionally oralternatively, the updated machine learning model may be transmitted toremote device and/or FPGA, wherein remote device and/or FPGA may replaceautonomous machine-learning model with updated machine-learning modeland generate the autonomous function as a function of flight element,pilot override, and/or simulation data using the updatedmachine-learning model. Updated machine-learning model may betransmitted by remote device and/or FPGA and received by flightcontroller as a software update, firmware update, or correctedautonomous machine-learning model. For example, and without limitationautonomous machine learning model may utilize a neural netmachine-learning process, wherein updated machine-learning model mayincorporate a gradient boosting machine-learning process.

Still referring to FIG. 4 , flight controller may include, be includedin, and/or communicate with a mobile device such as a mobile telephoneor smartphone. Further, flight controller may communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forcommutatively connecting a flight controller to one or more of a varietyof networks, and one or more devices. Examples of a network interfacedevice may include, but are not limited to, a network interface card(e.g., a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network may include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. Network may include any networktopology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 4 , flight controller mayinclude, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controllermay include one or more flight controllers dedicated to data storage,security, distribution of traffic for load balancing, and the like.Flight controller may be configured to distribute one or more computingtasks as described below across a plurality of flight controllers, whichmay operate in parallel, in series, redundantly, or in any other mannerused for distribution of tasks or memory between computing devices. Forexample, and without limitation, flight controller may implement acontrol algorithm to distribute and/or command plurality of flightcontrollers. As used in this disclosure a “control algorithm” is afinite sequence of well-defined computer implementable instructions thatmay determine flight component of plurality of flight components to beadjusted. For example, and without limitation, control algorithm mayinclude one or more algorithms that reduce and/or prevent aviationasymmetry. As a further non-limiting example, control algorithms mayinclude one or more models generated as a function of a softwareincluding, but not limited to Simulink by MathWorks, Natick, Mass., USA.In an embodiment, and without limitation, control algorithm may beconfigured to generate an auto-code, wherein an “auto-code,” is usedherein, is a code and/or algorithm that is generated as a function ofone or more models and/or software's. In another embodiment, controlalgorithm may be configured to produce a segmented control algorithm. Asused in this disclosure a “segmented control algorithm” is controlalgorithm that has been separated and/or parsed into discrete sections.For example, and without limitation, segmented control algorithm mayparse control algorithm into two or more segments, wherein each segmentof control algorithm may be performed by one or more flight controllersoperating on distinct flight components.

In an embodiment, and still referring to FIG. 4 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with segments of thesegmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 108. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furtherincludes separating a plurality of signal codes across plurality offlight controllers. For example, and without limitation, plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, plurality of flight controllers may include a chainpath, wherein a “chain path,” as used herein, is a linear communicationpath comprising a hierarchy that data may flow through. In anembodiment, and without limitation, plurality of flight controllers mayinclude an all-channel path, wherein an “all-channel path,” as usedherein, is a communication path that is not restricted to a particulardirection. For example, and without limitation, data may be transmittedupward, downward, laterally, and the like thereof. In an embodiment, andwithout limitation, plurality of flight controllers may include one ormore neural networks that assign a weighted value to a transmitteddatum. For example, and without limitation, a weighted value may beassigned as a function of one or more signals denoting that a flightcomponent is malfunctioning and/or in a failure state.

Still referring to FIG. 4 , plurality of flight controllers may includea master bus controller. As used in this disclosure a “master buscontroller” is one or more devices and/or components that are connectedto a bus to initiate a direct memory access transaction, wherein a busis one or more terminals in a bus architecture. Master bus controllermay communicate using synchronous and/or asynchronous bus controlprotocols. In an embodiment, master bus controller may include flightcontroller 116. In another embodiment, master bus controller may includeone or more universal asynchronous receiver-transmitters (UART). Forexample, and without limitation, master bus controller may include oneor more bus architectures that allow a bus to initiate a direct memoryaccess transaction from one or more buses in the bus architectures. As afurther non-limiting example, master bus controller may include one ormore peripheral devices and/or components to communicate with anotherperipheral device and/or component and/or master bus controller. In anembodiment, master bus controller may be configured to perform busarbitration. As used in this disclosure “bus arbitration” is methodand/or scheme to prevent multiple buses from attempting to communicatewith and/or connect to master bus controller. For example, and withoutlimitation, bus arbitration may include one or more schemes such as asmall computer interface system, wherein a small computer interfacesystem is a set of standards for physical connecting and transferringdata between peripheral devices and master bus controller by definingcommands, protocols, electrical, optical, and/or logical interfaces. Inan embodiment, master bus controller may receive intermediaterepresentation 408 and/or output language from logic component 416,wherein output language may include one or more analog-to-digitalconversions, low bit rate transmissions, message encryptions, digitalsignals, binary signals, logic signals, analog signals, and the likethereof described above in detail.

Still referring to FIG. 4 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 4 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of master bus control.As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated betweenplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 4 , flight controller may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to input nodes, asuitable training algorithm (such as Levenberg-Marquardt, conjugategradient, simulated annealing, or other algorithms) is then used toadjust connections and weights between nodes in adjacent layers ofneural network to produce the desired values at output nodes. Thisprocess is sometimes referred to as deep learning.

Still referring to FIG. 4 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing node and/or from other nodes. Node mayperform a weighted sum of inputs using weights w_(i) that are multipliedby respective inputs x_(i). Additionally or alternatively, a bias b maybe added to the weighted sum of inputs such that an offset is added toeach unit in neural network layer that is independent of input to thelayer. Weighted sum may then be input into a function φ, which maygenerate one or more outputs y. Weight w_(i) applied to an input x_(i)may indicate whether input is “excitatory,” indicating that it hasstrong influence on one or more outputs y, for instance by thecorresponding weight having a large numerical value, and/or a“inhibitory,” indicating it has a weak effect influence on one moreinputs y, for instance by the corresponding weight having a smallnumerical value. Values of weights w_(i) may be determined by training aneural network using training data, which may be performed using anysuitable process as described above. In an embodiment, and withoutlimitation, a neural network may receive semantic units as inputs andoutput vectors representing such semantic units according to weightsw_(i) that are derived using machine-learning processes as described inthis disclosure.

Still referring to FIG. 4 , flight controller may include asub-controller 432. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller may be and/or include adistributed flight controller made up of one or more sub-controllers.For example, and without limitation, sub-controller 432 may include anycontrollers and/or components thereof that are similar to distributedflight controller and/or flight controller as described above.Sub-controller 432 may include any component of any flight controller asdescribed above. Sub-controller 432 may be implemented in any mannersuitable for implementation of a flight controller as described above.As a further non-limiting example, sub-controller 432 may include one ormore processors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data across distributedflight controller as described above. As a further non-limiting example,sub-controller 432 may include a controller that receives a signal froma first flight controller and/or first distributed flight controllercomponent and transmits signal to a plurality of additionalsub-controllers and/or flight components.

Still referring to FIG. 4 , flight controller may include aco-controller 436. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller as componentsand/or nodes of a distributer flight controller as described above. Forexample, and without limitation, co-controller 436 may include one ormore controllers and/or components that are similar to flightcontroller. As a further non-limiting example, co-controller 436 mayinclude any controller and/or component that joins flight controller todistributer flight controller. As a further non-limiting example,co-controller 436 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller to distributed flightcontrol system. Co-controller 436 may include any component of anyflight controller as described above. Co-controller 436 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 4 , flightcontroller may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller may be configured to perform a single stepor sequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Flight controller may perform any step or sequence of steps asdescribed in this disclosure in parallel, such as simultaneously and/orsubstantially simultaneously performing a step two or more times usingtwo or more parallel threads, processor cores, or the like; division oftasks between parallel threads and/or processes may be performedaccording to any protocol suitable for division of tasks betweeniterations. Persons skilled in the art, upon reviewing entirety of thisdisclosure, will be aware of various ways in which steps, sequences ofsteps, processing tasks, and/or data may be subdivided, shared, orotherwise dealt with using iteration, recursion, and/or parallelprocessing.

Referring now to FIG. 5 , an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 512;this is in contrast to a non-machine learning software program wherecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 5 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5 ,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput is an autonomous function.

Further referring to FIG. 5 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier Training data classifier may include a “classifier,” which asused in this disclosure is a machine-learning model as defined below,such as a mathematical model, neural net, or program generated by amachine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 412 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 5 , machine-learning module 500 may beconfigured to perform a lazy-learning process 512 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining input and training set to derive thealgorithm to be used to produce output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 504. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 504 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing entirety of this disclosure, will beaware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 5 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above and stored in memory; an inputis submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via process of“training” the network, in which elements from a training data 504 setare applied to input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust connections and weights between nodesin adjacent layers of neural network to produce desired values at outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 5 , machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, may include algorithmsthat receive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 504. Persons skilled in the art, uponreviewing entirety of this disclosure, will be aware of various possiblevariations of at least a supervised machine-learning process 528 thatmay be used to determine relation between inputs and outputs. Supervisedmachine-learning processes may include classification algorithms asdefined above.

Further referring to FIG. 5 , machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5 , machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients ofresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized may include the least-squares function plusterm multiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Now referring to FIG. 6 , an exemplary embodiment of method 600 fordetermining distance in a simulation of an electric aircraft. Theelectric aircraft may include, without limitation, any of the aircraftas disclosed herein and described above with reference to at least FIG.1 , including an eVTOL aircraft.

Still referring to FIG. 6 , at step 605, an aircraft location datum 304is detected by sensor 112. The sensor includes but is not limited tooptical, ranging, environment sensors, or the like. Aircraft locationdatum 304 may include a longitude and latitude datum. The aircraftlocation datum may be any one of the datums as disclosed herein anddescribed above with reference to at least FIGS. 2-3 . The sensor may beany of the sensors as disclosed herein and described above withreference to at least FIGS. 1-3 .

Still referring to FIG. 6 , at step 610, a boundary datum 308 isdetected by sensor 112. Boundary datum may be, without limitation, anylatitude or longitudinal coordinates prep-programmed in the sensor to beon the boundary. The boundary cross-sectional area may be ellipse. Theboundary datum may be any one of the datums as disclosed herein anddescribed above with reference to at least FIGS. 2-3 . The sensor may beany of the sensors as disclosed herein and described above withreference to at least FIGS. 1-3 .

Still referring to FIG. 6 , at step 615, the aircraft location datum 304and boundary datum 308 are transmitted to computing device 116.Computing device 116 may be any type of flight controller, tablet, smartphone, computer, or the like. The aircraft location datum may be any oneof the datums as disclosed herein and described above with reference toat least FIGS. 2-3 . The boundary datum may be any one of the datums asdisclosed herein and described above with reference to at least FIGS.2-3 . The computing device may be any of the devices as disclosed hereinand described above with reference to at least FIGS. 1-3 .

Still referring to FIG. 6 , at step 620, the aircraft location datum 304and boundary datum 308 are received by computing device 116. Theaircraft location datum may be any one of the datums as disclosed hereinand described above with reference to at least FIGS. 2-3 . The boundarydatum may be any one of the datums as disclosed herein and describedabove with reference to at least FIGS. 2-3 . The computing device may beany of the devices as disclosed herein and described above withreference to at least FIGS. 1-3 .

Still referring to FIG. 6 , at step 625, a distance datum 316 betweenthe aircraft location and boundary is determined by the computing device116 as a function of the aircraft location datum 304 and boundary datum308. Distance datum may be, without limitation, any measurement like 300feet, 1.2 kilometers, 0.67 miles, 588 yards, or the like. The distancedatum may be any one of the datums as disclosed herein and describedabove with reference to at least FIG. 3 . The aircraft location datummay be any one of the datums as disclosed herein and described abovewith reference to at least FIGS. 2-3 . The boundary datum may be any oneof the datums as disclosed herein and described above with reference toat least FIGS. 2-3 . The computing device may be any of the devices asdisclosed herein and described above with reference to at least FIGS.1-3 .

Still referring to FIG. 6 , at step 630, a recommended aircraftadjustment 316 is generated at the computing device 116 as a function ofthe distance datum 312. Recommended aircraft adjustment may be, withoutlimitation: move left, make a turn, change direction 34 degrees east,turn around, or the like. The recommended aircraft adjustment may be anyone of the adjustments as disclosed herein and described above withreference to at least FIG. 3 . The distance datum may be any one of thedatums as disclosed herein and described above with reference to atleast FIG. 3 . The computing device may be any of the devices asdisclosed herein and described above with reference to at least FIGS.1-3 .

Still referring to FIG. 6 , at step 635, the recommended aircraftadjustment and distance datum are transmitted, at a computing device112, to remote device 320. Remote device 320 may be, without limitation,VR goggles, tablet, computer, pilot display, or the like. The remotedevice further presents a speed limit associated with thethree-dimensional flying space. Remote device 320 further presents apermitted direction of flight associated with the three-dimensionalflying space. The remote device is configured to alert the user if thedistance datum is below a distance threshold. Examples of a user may bea pilot, a simulation, a pilot in a remote setting, or the like. Theremote device tells the user the distance datum and recommended aircraftadjustment through a visual and auditory alert. The remote device isfurther configured to receive a user interaction and transmit it to thecomputing device. The recommended aircraft adjustment may be any one ofthe adjustments as disclosed herein and described above with referenceto at least FIG. 3 . The distance datum may be any one of the datums asdisclosed herein and described above with reference to at least FIG. 3 .The computing device may be any of the devices as disclosed herein anddescribed above with reference to at least FIGS. 1-3 . The remote devicemay be any of the devices as disclosed herein and described above withreference to at least FIG. 3 .

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in computer art. Appropriate software coding can readilybe prepared by skilled programmers based on the teachings of the presentdisclosure, as will be apparent to those of ordinary skill in thesoftware art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium may include, but are not limited to, a magnetic disk, an opticaldisc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, aread-only memory “ROM” device, a random-access memory “RAM” device, amagnetic card, an optical card, a solid-state memory device, an EPROM,an EEPROM, and any combinations thereof. A machine-readable medium, asused herein, may include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device may include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in exemplary form of a computer system 700 within whicha set of instructions for causing a control system to perform any one ormore of the aspects and/or methodologies of the present disclosure maybe executed. It is also contemplated that multiple computing devices maybe utilized to implement a specially configured set of instructions forcausing one or more of the devices to perform any one or more of theaspects and/or methodologies of the present disclosure. Computer system700 may include a processor 704 and a memory 708 that communicate witheach other, and with other components, via a bus 428432. Bus 428432 mayinclude any of several types of bus structures including, but notlimited to, a memory bus, a memory controller, a peripheral bus, a localbus, and any combinations thereof, using any of a variety of busarchitectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 428432by an appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown 208. Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 428432 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 428432, and any combinations thereof. Input device 732may include a touch screen interface that may be a part of or separatefrom display 736, discussed further below. Input device 732 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 708 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 428432 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate toprovide a multiplicity of feature combinations in associated newembodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve systems andmethods according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. A system for defining boundaries of a simulation of an electricaircraft, the system comprising: a sensor, wherein the sensor isconfigured to: detect an aircraft location datum of a manned electricaircraft; detect a boundary datum associated with a three-dimensionalflying space, the detecting of the boundary datum comprising:determining a closest point of a plurality of points on a boundary basedon the aircraft location datum; and detecting the boundary datum as afunction of the closest point of the boundary; and transmit the aircraftlocation datum and boundary datum to a computing device; the computingdevice, wherein the computing device is configured to: receive theaircraft location datum and boundary datum from the sensor; determine adistance datum between the aircraft location and as a function of theaircraft location datum and boundary datum; generate a recommendedaircraft adjustment as a function of the distance datum; and transmitthe distance datum and recommended aircraft adjustment to a pilotdisplay located in the electric aircraft.
 2. The system of claim 1,wherein the aircraft location datum includes: a longitude datum; and alatitude datum.
 3. The system of claim 1, wherein the three-dimensionalflying space has an elliptical cross section.
 4. The system of claim 1,wherein the boundary datum is configured to include a longitude datumand a latitude datum that are pre-programmed into the computing deviceto represent the 3D flying space.
 5. The system of claim 1, wherein theremote device further presents a speed limit associated with thethree-dimensional flying space.
 6. The system of claim 1, wherein theremote device further presents a permitted direction of flightassociated with the three-dimensional flying space.
 7. The system ofclaim 1, wherein the remote device tells the user the distance datum andrecommended aircraft adjustment through a visual and auditory alert. 8.The system of claim 1, wherein the remote device is further configuredto: receive a user interaction; and transmit the user interaction to thecomputing device.
 9. The system of claim 8, wherein the computing deviceis further configured to: receive the user interaction from the remotedevice; generate an updated recommended aircraft adjustment as afunction of the user interaction; and transmit the updated recommendedaircraft adjustment to the remote device.
 10. The system of claim 1,wherein the remote device is configured to alert the user if thedistance datum is below a distance threshold.
 11. A method for definingboundaries of a simulation of an electric aircraft, the methodcomprising: detecting, at a sensor, an aircraft location datum of amanned aircraft; detecting, at the sensor, a boundary datum associatedwith a three-dimensional flying space for the aircraft, the detecting ofthe boundary datum comprising: determining a closest point of aplurality of points on a boundary based on the aircraft location datum;and detecting the boundary datum as a function of the closest point ofthe boundary; transmitting, at the sensor, the aircraft location datumand boundary datum to a flight controller; receiving, at the flightcontroller, the aircraft location datum and boundary datum from thesensor; determining, at the flight controller, a distance datum betweenthe aircraft location and boundary as a function of the aircraftlocation datum and boundary datum; generating, at the flight controller,a recommended aircraft adjustment as a function of the distance datum;transmitting, at the flight controller, the distance datum andrecommended aircraft adjustment to a pilot display located in theelectric aircraft.
 12. The method of claim 11, wherein the aircraftlocation datum is configured to include: a longitude datum; and alatitude datum.
 13. The method of claim 11, wherein thethree-dimensional flying space has an elliptical cross section.
 14. Themethod of claim 11, wherein the boundary datum is configured to includea longitude datum and a latitude datum that are pre-programmed into thecomputing device to represent the 3D flying space.
 15. The method ofclaim 11, wherein the remote device further presents a speed limitassociated with the three-dimensional flying space.
 16. The method ofclaim 11, wherein the remote device further presents a permitteddirection of flight associated with the three-dimensional flying space.17. The method of claim 11, wherein the remote device tells the user thedistance datum and recommended aircraft adjustment through a visual andauditory alert.
 18. The method of claim 11, wherein the remote device isfurther configured to: receive a user interaction; and transmit the userinteraction to the computing device.
 19. The method of claim 18, whereinthe computing device is further configured to: receive the userinteraction from the remote device; generate an updated recommendedaircraft adjustment as a function of the user interaction; and transmitthe updated recommended aircraft adjustment to the remote device. 20.The method of claim 11, wherein the remote device is configured to alertthe user if the distance datum is below a distance threshold.